Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations129
Missing cells13
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.7 KiB
Average record size in memory196.0 B

Variable types

Categorical5
Numeric18
Boolean4

Alerts

calidad_de_agua is highly overall correlated with icaHigh correlation
campaña is highly overall correlated with cd_total_mg_l_menor_que and 4 other fieldsHigh correlation
cd_total_mg_l_menor_que is highly overall correlated with campaña and 3 other fieldsHigh correlation
clorofila_a_ug_l is highly overall correlated with dbo_mg_lHigh correlation
colif_fecales_ufc_100ml is highly overall correlated with enteroc_ufc_100ml and 3 other fieldsHigh correlation
color is highly overall correlated with ica and 2 other fieldsHigh correlation
cr_total_mg_l is highly overall correlated with dqo_mg_lHigh correlation
dbo_mg_l is highly overall correlated with clorofila_a_ug_lHigh correlation
dqo_mg_l is highly overall correlated with campaña and 3 other fieldsHigh correlation
enteroc_ufc_100ml is highly overall correlated with colif_fecales_ufc_100ml and 2 other fieldsHigh correlation
escher_coli_ufc_100ml is highly overall correlated with colif_fecales_ufc_100ml and 2 other fieldsHigh correlation
espumas is highly overall correlated with colif_fecales_ufc_100ml and 2 other fieldsHigh correlation
fosf_ortofos_mg_l is highly overall correlated with ica and 2 other fieldsHigh correlation
hidr_deriv_petr_ug_l is highly overall correlated with cd_total_mg_l_menor_queHigh correlation
ica is highly overall correlated with calidad_de_agua and 9 other fieldsHigh correlation
microcistina_ug_l is highly overall correlated with campaña and 2 other fieldsHigh correlation
nh4_mg_l is highly overall correlated with espumas and 3 other fieldsHigh correlation
od is highly overall correlated with ica and 2 other fieldsHigh correlation
olores is highly overall correlated with color and 2 other fieldsHigh correlation
p_total_l_mg_l is highly overall correlated with fosf_ortofos_mg_lHigh correlation
ph is highly overall correlated with odHigh correlation
sitios is highly overall correlated with color and 1 other fieldsHigh correlation
tem_agua is highly overall correlated with campaña and 1 other fieldsHigh correlation
tem_aire is highly overall correlated with campaña and 1 other fieldsHigh correlation
espumas is highly imbalanced (66.5%) Imbalance
dbo_mg_l has 6 (4.7%) missing values Missing
dqo_mg_l has 6 (4.7%) missing values Missing
sitios is uniformly distributed Uniform
clorofila_a_ug_l has 11 (8.5%) zeros Zeros

Reproduction

Analysis started2024-11-03 20:50:01.687081
Analysis finished2024-11-03 20:51:10.077015
Duration1 minute and 8.39 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

sitios
Categorical

High correlation  Uniform 

Distinct36
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Canal Villanueva y Río Luján
 
4
Canal Aliviador y Río Lujan
 
4
Río Carapachay y Arroyo Gallo Fiambre
 
4
Río Reconquista y Río Lujan
 
4
Playa La Balandra
 
4
Other values (31)
109 

Length

Max length37
Median length28
Mean length22.775194
Min length8

Characters and Unicode

Total characters2938
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanal Villanueva y Río Luján
2nd rowCanal Villanueva y Río Luján
3rd rowCanal Villanueva y Río Luján
4th rowCanal Villanueva y Río Luján
5th rowRío Lujan y Arroyo Caraguatá

Common Values

ValueCountFrequency (%)
Canal Villanueva y Río Luján 4
 
3.1%
Canal Aliviador y Río Lujan 4
 
3.1%
Río Carapachay y Arroyo Gallo Fiambre 4
 
3.1%
Río Reconquista y Río Lujan 4
 
3.1%
Playa La Balandra 4
 
3.1%
Rio Tigre 100m antes del Rio Luján 4
 
3.1%
Río Lujan y Canal San Fernando 4
 
3.1%
Playa Espigón de Pacheco 4
 
3.1%
Reserva Ecológica 4
 
3.1%
Puerto de Olivos Espigón 4
 
3.1%
Other values (26) 89
69.0%

Length

2024-11-03T17:51:10.374460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
y 36
 
6.9%
río 33
 
6.4%
de 22
 
4.2%
arroyo 20
 
3.9%
lujan 15
 
2.9%
canal 12
 
2.3%
400 12
 
2.3%
m 12
 
2.3%
playa 11
 
2.1%
calle 11
 
2.1%
Other values (80) 335
64.5%

Most occurring characters

ValueCountFrequency (%)
409
 
13.9%
a 347
 
11.8%
o 236
 
8.0%
e 168
 
5.7%
r 159
 
5.4%
l 153
 
5.2%
n 150
 
5.1%
i 120
 
4.1%
u 79
 
2.7%
c 76
 
2.6%
Other values (45) 1041
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2053
69.9%
Space Separator 409
 
13.9%
Uppercase Letter 391
 
13.3%
Decimal Number 76
 
2.6%
Dash Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%
Close Punctuation 3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 347
16.9%
o 236
11.5%
e 168
 
8.2%
r 159
 
7.7%
l 153
 
7.5%
n 150
 
7.3%
i 120
 
5.8%
u 79
 
3.8%
c 76
 
3.7%
y 75
 
3.7%
Other values (17) 490
23.9%
Uppercase Letter
ValueCountFrequency (%)
C 58
14.8%
P 58
14.8%
R 56
14.3%
E 34
8.7%
A 33
8.4%
L 30
7.7%
S 26
6.6%
B 20
 
5.1%
D 15
 
3.8%
T 12
 
3.1%
Other values (8) 49
12.5%
Decimal Number
ValueCountFrequency (%)
0 32
42.1%
4 19
25.0%
6 10
 
13.2%
1 7
 
9.2%
3 4
 
5.3%
7 4
 
5.3%
Space Separator
ValueCountFrequency (%)
409
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2444
83.2%
Common 494
 
16.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 347
 
14.2%
o 236
 
9.7%
e 168
 
6.9%
r 159
 
6.5%
l 153
 
6.3%
n 150
 
6.1%
i 120
 
4.9%
u 79
 
3.2%
c 76
 
3.1%
y 75
 
3.1%
Other values (35) 881
36.0%
Common
ValueCountFrequency (%)
409
82.8%
0 32
 
6.5%
4 19
 
3.8%
6 10
 
2.0%
1 7
 
1.4%
3 4
 
0.8%
7 4
 
0.8%
- 3
 
0.6%
( 3
 
0.6%
) 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2859
97.3%
None 79
 
2.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
409
14.3%
a 347
 
12.1%
o 236
 
8.3%
e 168
 
5.9%
r 159
 
5.6%
l 153
 
5.4%
n 150
 
5.2%
i 120
 
4.2%
u 79
 
2.8%
c 76
 
2.7%
Other values (41) 962
33.6%
None
ValueCountFrequency (%)
í 42
53.2%
á 18
22.8%
ó 15
 
19.0%
ú 4
 
5.1%

campaña
Categorical

High correlation 

Distinct4
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
primavera
36 
invierno
35 
otoño
29 
verano
29 

Length

Max length9
Median length8
Mean length7.1550388
Min length5

Characters and Unicode

Total characters923
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowverano
2nd rowotoño
3rd rowinvierno
4th rowprimavera
5th rowotoño

Common Values

ValueCountFrequency (%)
primavera 36
27.9%
invierno 35
27.1%
otoño 29
22.5%
verano 29
22.5%

Length

2024-11-03T17:51:10.835011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T17:51:11.178085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
primavera 36
27.9%
invierno 35
27.1%
otoño 29
22.5%
verano 29
22.5%

Most occurring characters

ValueCountFrequency (%)
o 151
16.4%
r 136
14.7%
i 106
11.5%
a 101
10.9%
e 100
10.8%
v 100
10.8%
n 99
10.7%
p 36
 
3.9%
m 36
 
3.9%
t 29
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 923
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 151
16.4%
r 136
14.7%
i 106
11.5%
a 101
10.9%
e 100
10.8%
v 100
10.8%
n 99
10.7%
p 36
 
3.9%
m 36
 
3.9%
t 29
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 923
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 151
16.4%
r 136
14.7%
i 106
11.5%
a 101
10.9%
e 100
10.8%
v 100
10.8%
n 99
10.7%
p 36
 
3.9%
m 36
 
3.9%
t 29
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 894
96.9%
None 29
 
3.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 151
16.9%
r 136
15.2%
i 106
11.9%
a 101
11.3%
e 100
11.2%
v 100
11.2%
n 99
11.1%
p 36
 
4.0%
m 36
 
4.0%
t 29
 
3.2%
None
ValueCountFrequency (%)
ñ 29
100.0%

tem_agua
Real number (ℝ)

High correlation 

Distinct71
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.699225
Minimum7
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:11.507000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile10.4
Q114.4
median16.7
Q324.5
95-th percentile26
Maximum28
Range21
Interquartile range (IQR)10.1

Descriptive statistics

Standard deviation5.6489006
Coefficient of variation (CV)0.30209277
Kurtosis-1.3721655
Mean18.699225
Median Absolute Deviation (MAD)5.3
Skewness-0.02356982
Sum2412.2
Variance31.910078
MonotonicityNot monotonic
2024-11-03T17:51:11.776764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.8 5
 
3.9%
26 5
 
3.9%
24.7 5
 
3.9%
10 4
 
3.1%
14.4 4
 
3.1%
14.5 4
 
3.1%
11 4
 
3.1%
13 3
 
2.3%
25.5 3
 
2.3%
15 3
 
2.3%
Other values (61) 89
69.0%
ValueCountFrequency (%)
7 2
1.6%
8 1
 
0.8%
10 4
3.1%
11 4
3.1%
11.4 1
 
0.8%
11.9 1
 
0.8%
12 2
1.6%
12.1 1
 
0.8%
12.8 1
 
0.8%
12.9 2
1.6%
ValueCountFrequency (%)
28 1
 
0.8%
27.8 1
 
0.8%
27.5 1
 
0.8%
27 2
 
1.6%
26.7 1
 
0.8%
26 5
3.9%
25.8 1
 
0.8%
25.7 1
 
0.8%
25.6 1
 
0.8%
25.5 3
2.3%

tem_aire
Real number (ℝ)

High correlation 

Distinct37
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.931008
Minimum4
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:12.045806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7.4
Q112.6
median18
Q327
95-th percentile31.6
Maximum33
Range29
Interquartile range (IQR)14.4

Descriptive statistics

Standard deviation8.0353302
Coefficient of variation (CV)0.42445338
Kurtosis-1.3033057
Mean18.931008
Median Absolute Deviation (MAD)7
Skewness0.060016673
Sum2442.1
Variance64.566531
MonotonicityNot monotonic
2024-11-03T17:51:12.328080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
28 11
 
8.5%
26 10
 
7.8%
11 9
 
7.0%
27 9
 
7.0%
13 7
 
5.4%
14.5 6
 
4.7%
12 6
 
4.7%
32 5
 
3.9%
8 5
 
3.9%
19 5
 
3.9%
Other values (27) 56
43.4%
ValueCountFrequency (%)
4 4
3.1%
7 3
 
2.3%
8 5
3.9%
9 2
 
1.6%
10 2
 
1.6%
11 9
7.0%
12 6
4.7%
12.5 1
 
0.8%
12.6 1
 
0.8%
12.8 4
3.1%
ValueCountFrequency (%)
33 1
 
0.8%
32.5 1
 
0.8%
32 5
3.9%
31 2
 
1.6%
29.1 3
 
2.3%
29 2
 
1.6%
28 11
8.5%
27.5 1
 
0.8%
27 9
7.0%
26 10
7.8%

od
Real number (ℝ)

High correlation 

Distinct117
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6376744
Minimum0.59
Maximum15.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:12.622772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.59
5-th percentile1.83
Q14.24
median6.12
Q38.9
95-th percentile12.47
Maximum15.2
Range14.61
Interquartile range (IQR)4.66

Descriptive statistics

Standard deviation3.1773273
Coefficient of variation (CV)0.4786808
Kurtosis-0.52126653
Mean6.6376744
Median Absolute Deviation (MAD)2.25
Skewness0.34225612
Sum856.26
Variance10.095409
MonotonicityNot monotonic
2024-11-03T17:51:12.851556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.91 2
 
1.6%
4.82 2
 
1.6%
6.2 2
 
1.6%
4.84 2
 
1.6%
5.95 2
 
1.6%
3.6 2
 
1.6%
8.2 2
 
1.6%
4.07 2
 
1.6%
4.24 2
 
1.6%
12.47 2
 
1.6%
Other values (107) 109
84.5%
ValueCountFrequency (%)
0.59 1
0.8%
0.74 1
0.8%
0.82 1
0.8%
1.21 1
0.8%
1.46 1
0.8%
1.61 1
0.8%
1.81 1
0.8%
1.86 1
0.8%
2.16 1
0.8%
2.3 1
0.8%
ValueCountFrequency (%)
15.2 1
0.8%
13.18 1
0.8%
12.72 1
0.8%
12.67 1
0.8%
12.65 1
0.8%
12.61 1
0.8%
12.47 2
1.6%
12.39 1
0.8%
12.22 1
0.8%
12.04 1
0.8%

ph
Real number (ℝ)

High correlation 

Distinct94
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5945736
Minimum6.66
Maximum9.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:13.065283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.66
5-th percentile6.754
Q16.96
median7.47
Q37.96
95-th percentile9.226
Maximum9.66
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.75445138
Coefficient of variation (CV)0.099340847
Kurtosis0.31162024
Mean7.5945736
Median Absolute Deviation (MAD)0.51
Skewness1.0215024
Sum979.7
Variance0.56919689
MonotonicityNot monotonic
2024-11-03T17:51:13.255739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 4
 
3.1%
6.82 4
 
3.1%
6.96 3
 
2.3%
7.59 3
 
2.3%
6.71 2
 
1.6%
6.79 2
 
1.6%
7.04 2
 
1.6%
6.92 2
 
1.6%
8.01 2
 
1.6%
7.69 2
 
1.6%
Other values (84) 103
79.8%
ValueCountFrequency (%)
6.66 1
 
0.8%
6.7 1
 
0.8%
6.71 2
1.6%
6.74 1
 
0.8%
6.75 2
1.6%
6.76 2
1.6%
6.77 1
 
0.8%
6.79 2
1.6%
6.8 4
3.1%
6.81 1
 
0.8%
ValueCountFrequency (%)
9.66 1
0.8%
9.44 2
1.6%
9.36 1
0.8%
9.32 1
0.8%
9.25 1
0.8%
9.23 1
0.8%
9.22 1
0.8%
9.19 1
0.8%
9.18 2
1.6%
9.07 1
0.8%

olores
Boolean

High correlation 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
111 
True
18 
ValueCountFrequency (%)
False 111
86.0%
True 18
 
14.0%
2024-11-03T17:51:13.443931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

color
Boolean

High correlation 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
112 
True
17 
ValueCountFrequency (%)
False 112
86.8%
True 17
 
13.2%
2024-11-03T17:51:13.610940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

espumas
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
121 
True
 
8
ValueCountFrequency (%)
False 121
93.8%
True 8
 
6.2%
2024-11-03T17:51:13.742290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

mat_susp
Boolean

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
100 
True
29 
ValueCountFrequency (%)
False 100
77.5%
True 29
 
22.5%
2024-11-03T17:51:13.911835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

colif_fecales_ufc_100ml
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18861.395
Minimum100
Maximum190000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:14.129705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile340
Q12000
median5000
Q315000
95-th percentile95200
Maximum190000
Range189900
Interquartile range (IQR)13000

Descriptive statistics

Standard deviation34231.665
Coefficient of variation (CV)1.8149063
Kurtosis9.3687733
Mean18861.395
Median Absolute Deviation (MAD)4000
Skewness2.9456386
Sum2433120
Variance1.1718069 × 109
MonotonicityNot monotonic
2024-11-03T17:51:14.389867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4000 10
 
7.8%
10000 8
 
6.2%
5000 8
 
6.2%
3000 7
 
5.4%
1000 7
 
5.4%
400 6
 
4.7%
2000 5
 
3.9%
20000 5
 
3.9%
80000 4
 
3.1%
15000 4
 
3.1%
Other values (47) 65
50.4%
ValueCountFrequency (%)
100 1
 
0.8%
110 1
 
0.8%
160 1
 
0.8%
300 4
3.1%
400 6
4.7%
500 1
 
0.8%
600 1
 
0.8%
700 1
 
0.8%
800 1
 
0.8%
1000 7
5.4%
ValueCountFrequency (%)
190000 1
 
0.8%
180000 1
 
0.8%
140000 1
 
0.8%
120000 1
 
0.8%
100000 3
2.3%
88000 1
 
0.8%
82000 1
 
0.8%
80000 4
3.1%
60000 1
 
0.8%
42000 1
 
0.8%

escher_coli_ufc_100ml
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4877.5581
Minimum5
Maximum70000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:14.655447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile38
Q1200
median1000
Q33000
95-th percentile23000
Maximum70000
Range69995
Interquartile range (IQR)2800

Descriptive statistics

Standard deviation11107.844
Coefficient of variation (CV)2.2773371
Kurtosis16.62528
Mean4877.5581
Median Absolute Deviation (MAD)900
Skewness3.9190527
Sum629205
Variance1.233842 × 108
MonotonicityNot monotonic
2024-11-03T17:51:14.903751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 14
 
10.9%
1000 13
 
10.1%
2000 11
 
8.5%
200 6
 
4.7%
500 5
 
3.9%
20 5
 
3.9%
400 5
 
3.9%
10000 4
 
3.1%
1500 4
 
3.1%
3000 4
 
3.1%
Other values (43) 58
45.0%
ValueCountFrequency (%)
5 1
 
0.8%
20 5
 
3.9%
30 1
 
0.8%
50 2
 
1.6%
80 1
 
0.8%
100 14
10.9%
120 1
 
0.8%
150 1
 
0.8%
170 1
 
0.8%
200 6
4.7%
ValueCountFrequency (%)
70000 1
 
0.8%
60000 1
 
0.8%
50000 1
 
0.8%
48000 1
 
0.8%
37000 1
 
0.8%
30000 1
 
0.8%
25000 1
 
0.8%
20000 2
1.6%
18000 1
 
0.8%
12000 3
2.3%

enteroc_ufc_100ml
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean867.29457
Minimum2
Maximum28000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:15.132238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q158
median200
Q3700
95-th percentile3420
Maximum28000
Range27998
Interquartile range (IQR)642

Descriptive statistics

Standard deviation2662.0142
Coefficient of variation (CV)3.0693311
Kurtosis85.632495
Mean867.29457
Median Absolute Deviation (MAD)173
Skewness8.5867848
Sum111881
Variance7086319.8
MonotonicityNot monotonic
2024-11-03T17:51:15.329179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 13
 
10.1%
40 6
 
4.7%
300 6
 
4.7%
10 5
 
3.9%
30 5
 
3.9%
20 5
 
3.9%
800 5
 
3.9%
500 4
 
3.1%
200 3
 
2.3%
90 3
 
2.3%
Other values (63) 74
57.4%
ValueCountFrequency (%)
2 1
 
0.8%
4 1
 
0.8%
5 2
 
1.6%
6 1
 
0.8%
10 5
3.9%
12 1
 
0.8%
15 1
 
0.8%
20 5
3.9%
27 1
 
0.8%
30 5
3.9%
ValueCountFrequency (%)
28000 1
0.8%
6200 1
0.8%
5600 1
0.8%
4800 1
0.8%
4600 2
1.6%
3700 1
0.8%
3000 1
0.8%
2800 1
0.8%
2300 1
0.8%
2100 2
1.6%

nitrato_mg_l
Real number (ℝ)

Distinct52
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2372093
Minimum2
Maximum22.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:15.561836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12.3
median3.5
Q35.4
95-th percentile7.34
Maximum22.1
Range20.1
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation2.7848381
Coefficient of variation (CV)0.65723403
Kurtosis21.895599
Mean4.2372093
Median Absolute Deviation (MAD)1.4
Skewness3.8785653
Sum546.6
Variance7.7553234
MonotonicityNot monotonic
2024-11-03T17:51:15.939913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 22
 
17.1%
3.1 6
 
4.7%
2.1 5
 
3.9%
2.9 5
 
3.9%
3.5 5
 
3.9%
2.2 4
 
3.1%
3 3
 
2.3%
3.2 3
 
2.3%
3.4 3
 
2.3%
2.8 3
 
2.3%
Other values (42) 70
54.3%
ValueCountFrequency (%)
2 22
17.1%
2.1 5
 
3.9%
2.2 4
 
3.1%
2.3 2
 
1.6%
2.5 1
 
0.8%
2.6 1
 
0.8%
2.7 1
 
0.8%
2.8 3
 
2.3%
2.9 5
 
3.9%
3 3
 
2.3%
ValueCountFrequency (%)
22.1 1
0.8%
21 1
0.8%
9 1
0.8%
8.2 1
0.8%
8.1 1
0.8%
7.9 1
0.8%
7.5 1
0.8%
7.1 1
0.8%
7 2
1.6%
6.9 1
0.8%

nh4_mg_l
Real number (ℝ)

High correlation 

Distinct78
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2243411
Minimum0.05
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:16.359046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.1
Q10.44
median0.98
Q32.8
95-th percentile6.54
Maximum39
Range38.95
Interquartile range (IQR)2.36

Descriptive statistics

Standard deviation4.0415341
Coefficient of variation (CV)1.8169579
Kurtosis54.317428
Mean2.2243411
Median Absolute Deviation (MAD)0.78
Skewness6.4317905
Sum286.94
Variance16.333998
MonotonicityNot monotonic
2024-11-03T17:51:16.915724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 6
 
4.7%
0.7 5
 
3.9%
0.6 4
 
3.1%
0.3 4
 
3.1%
1.1 4
 
3.1%
3.1 4
 
3.1%
0.9 3
 
2.3%
1.8 3
 
2.3%
0.8 3
 
2.3%
1 3
 
2.3%
Other values (68) 90
69.8%
ValueCountFrequency (%)
0.05 2
 
1.6%
0.06 2
 
1.6%
0.08 1
 
0.8%
0.09 1
 
0.8%
0.1 6
4.7%
0.11 1
 
0.8%
0.14 1
 
0.8%
0.15 1
 
0.8%
0.16 1
 
0.8%
0.2 3
2.3%
ValueCountFrequency (%)
39 1
0.8%
14 1
0.8%
13 1
0.8%
9.7 1
0.8%
9.3 1
0.8%
7 1
0.8%
6.9 1
0.8%
6 1
0.8%
5.7 1
0.8%
5.6 1
0.8%

p_total_l_mg_l
Real number (ℝ)

High correlation 

Distinct63
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55992248
Minimum0.19
Maximum1.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:17.405794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile0.2
Q10.29
median0.46
Q30.75
95-th percentile1.3
Maximum1.9
Range1.71
Interquartile range (IQR)0.46

Descriptive statistics

Standard deviation0.35826076
Coefficient of variation (CV)0.63983994
Kurtosis1.6990153
Mean0.55992248
Median Absolute Deviation (MAD)0.19
Skewness1.3755661
Sum72.23
Variance0.12835078
MonotonicityNot monotonic
2024-11-03T17:51:18.666958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 11
 
8.5%
0.36 7
 
5.4%
1.1 4
 
3.1%
0.31 4
 
3.1%
0.75 4
 
3.1%
0.22 4
 
3.1%
0.35 3
 
2.3%
0.25 3
 
2.3%
0.32 3
 
2.3%
0.73 3
 
2.3%
Other values (53) 83
64.3%
ValueCountFrequency (%)
0.19 2
 
1.6%
0.2 11
8.5%
0.21 2
 
1.6%
0.22 4
 
3.1%
0.23 1
 
0.8%
0.24 1
 
0.8%
0.25 3
 
2.3%
0.26 2
 
1.6%
0.27 3
 
2.3%
0.28 3
 
2.3%
ValueCountFrequency (%)
1.9 1
 
0.8%
1.6 2
1.6%
1.5 2
1.6%
1.4 1
 
0.8%
1.3 2
1.6%
1.2 2
1.6%
1.1 4
3.1%
1 2
1.6%
0.98 1
 
0.8%
0.94 1
 
0.8%

fosf_ortofos_mg_l
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43666667
Minimum0.1
Maximum1.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:18.900887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.11
Q10.26
median0.36
Q30.53
95-th percentile1.1
Maximum1.5
Range1.4
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.28248709
Coefficient of variation (CV)0.64691701
Kurtosis3.3008841
Mean0.43666667
Median Absolute Deviation (MAD)0.14
Skewness1.7019257
Sum56.33
Variance0.079798958
MonotonicityNot monotonic
2024-11-03T17:51:19.167166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 7
 
5.4%
0.2 5
 
3.9%
0.1 5
 
3.9%
0.29 5
 
3.9%
0.37 5
 
3.9%
0.51 5
 
3.9%
0.36 4
 
3.1%
0.27 3
 
2.3%
0.44 3
 
2.3%
0.32 3
 
2.3%
Other values (51) 84
65.1%
ValueCountFrequency (%)
0.1 5
3.9%
0.11 3
2.3%
0.12 1
 
0.8%
0.13 1
 
0.8%
0.14 2
 
1.6%
0.15 1
 
0.8%
0.16 1
 
0.8%
0.18 1
 
0.8%
0.19 2
 
1.6%
0.2 5
3.9%
ValueCountFrequency (%)
1.5 2
1.6%
1.3 1
 
0.8%
1.2 3
2.3%
1.1 2
1.6%
0.96 1
 
0.8%
0.94 1
 
0.8%
0.87 1
 
0.8%
0.77 1
 
0.8%
0.76 1
 
0.8%
0.75 1
 
0.8%

dbo_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct60
Distinct (%)48.8%
Missing6
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean4.9666667
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:19.436969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12.7
median4.3
Q36.65
95-th percentile9.98
Maximum14
Range12
Interquartile range (IQR)3.95

Descriptive statistics

Standard deviation2.7587318
Coefficient of variation (CV)0.55544935
Kurtosis0.91342951
Mean4.9666667
Median Absolute Deviation (MAD)2
Skewness1.0685688
Sum610.9
Variance7.6106011
MonotonicityNot monotonic
2024-11-03T17:51:19.837853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 19
 
14.7%
2.1 4
 
3.1%
3 4
 
3.1%
4.1 3
 
2.3%
6 3
 
2.3%
4.8 3
 
2.3%
5.5 3
 
2.3%
2.7 3
 
2.3%
5.8 3
 
2.3%
4.2 3
 
2.3%
Other values (50) 75
58.1%
(Missing) 6
 
4.7%
ValueCountFrequency (%)
2 19
14.7%
2.1 4
 
3.1%
2.2 2
 
1.6%
2.3 2
 
1.6%
2.4 2
 
1.6%
2.5 1
 
0.8%
2.7 3
 
2.3%
2.8 1
 
0.8%
2.9 1
 
0.8%
3 4
 
3.1%
ValueCountFrequency (%)
14 1
0.8%
13 2
1.6%
12 2
1.6%
11 1
0.8%
10 1
0.8%
9.8 1
0.8%
9.4 1
0.8%
8.9 2
1.6%
8.8 1
0.8%
8.7 1
0.8%

dqo_mg_l
Real number (ℝ)

High correlation  Missing 

Distinct49
Distinct (%)39.8%
Missing6
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean34.295935
Minimum2.2
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:20.186896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile4.36
Q130
median30
Q337
95-th percentile72.4
Maximum140
Range137.8
Interquartile range (IQR)7

Descriptive statistics

Standard deviation23.055729
Coefficient of variation (CV)0.67225835
Kurtosis6.0485417
Mean34.295935
Median Absolute Deviation (MAD)5
Skewness1.8679842
Sum4218.4
Variance531.56662
MonotonicityNot monotonic
2024-11-03T17:51:20.517039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
30 52
40.3%
37 5
 
3.9%
31 3
 
2.3%
5.6 3
 
2.3%
32 3
 
2.3%
43 2
 
1.6%
5.2 2
 
1.6%
34 2
 
1.6%
35 2
 
1.6%
57 2
 
1.6%
Other values (39) 47
36.4%
(Missing) 6
 
4.7%
ValueCountFrequency (%)
2.2 1
0.8%
2.8 1
0.8%
3.3 1
0.8%
3.9 1
0.8%
4.1 1
0.8%
4.3 2
1.6%
4.9 1
0.8%
5 1
0.8%
5.1 1
0.8%
5.2 2
1.6%
ValueCountFrequency (%)
140 1
0.8%
125 2
1.6%
88 1
0.8%
82 1
0.8%
73 2
1.6%
67 1
0.8%
65 1
0.8%
64 1
0.8%
62 2
1.6%
59 1
0.8%

turbiedad_ntu
Real number (ℝ)

Distinct51
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.90155
Minimum4.9
Maximum210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:20.869322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile7.3
Q116
median27
Q331
95-th percentile66
Maximum210
Range205.1
Interquartile range (IQR)15

Descriptive statistics

Standard deviation23.438087
Coefficient of variation (CV)0.81096295
Kurtosis28.760623
Mean28.90155
Median Absolute Deviation (MAD)8
Skewness4.3388164
Sum3728.3
Variance549.3439
MonotonicityNot monotonic
2024-11-03T17:51:21.196948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 25
 
19.4%
16 6
 
4.7%
21 5
 
3.9%
50 5
 
3.9%
31 4
 
3.1%
13 4
 
3.1%
22 4
 
3.1%
33 4
 
3.1%
23 4
 
3.1%
29 4
 
3.1%
Other values (41) 64
49.6%
ValueCountFrequency (%)
4.9 1
0.8%
5.2 1
0.8%
5.3 1
0.8%
5.9 1
0.8%
6 1
0.8%
7 1
0.8%
7.1 1
0.8%
7.6 1
0.8%
8.7 1
0.8%
9 1
0.8%
ValueCountFrequency (%)
210 1
 
0.8%
120 1
 
0.8%
80 1
 
0.8%
75 2
 
1.6%
70 2
 
1.6%
60 1
 
0.8%
55 1
 
0.8%
50 5
3.9%
45 3
2.3%
43 1
 
0.8%

hidr_deriv_petr_ug_l
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.937209
Minimum6.9
Maximum340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:21.463984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile54
Q1100
median100
Q3100
95-th percentile116
Maximum340
Range333.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation34.419755
Coefficient of variation (CV)0.34441381
Kurtosis22.430269
Mean99.937209
Median Absolute Deviation (MAD)0
Skewness3.4948377
Sum12891.9
Variance1184.7195
MonotonicityNot monotonic
2024-11-03T17:51:21.731910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
100 100
77.5%
110 3
 
2.3%
70 2
 
1.6%
75 2
 
1.6%
45 2
 
1.6%
60 2
 
1.6%
65 2
 
1.6%
80 2
 
1.6%
50 2
 
1.6%
85 1
 
0.8%
Other values (11) 11
 
8.5%
ValueCountFrequency (%)
6.9 1
0.8%
16 1
0.8%
39 1
0.8%
45 2
1.6%
50 2
1.6%
60 2
1.6%
65 2
1.6%
70 2
1.6%
75 2
1.6%
80 2
1.6%
ValueCountFrequency (%)
340 1
 
0.8%
250 1
 
0.8%
220 1
 
0.8%
210 1
 
0.8%
150 1
 
0.8%
140 1
 
0.8%
120 1
 
0.8%
110 3
 
2.3%
100 100
77.5%
95 1
 
0.8%

cr_total_mg_l
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)6.2%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean0.79673281
Minimum0.005
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:21.984360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.005
Q10.005
median0.005
Q30.254275
95-th percentile1
Maximum44
Range43.995
Interquartile range (IQR)0.249275

Descriptive statistics

Standard deviation4.1509701
Coefficient of variation (CV)5.2099901
Kurtosis94.589521
Mean0.79673281
Median Absolute Deviation (MAD)0
Skewness9.3101127
Sum101.9818
Variance17.230553
MonotonicityNot monotonic
2024-11-03T17:51:22.189117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.005 92
71.3%
1 28
 
21.7%
0.0052 2
 
1.6%
0.0057 2
 
1.6%
44 1
 
0.8%
7.8 1
 
0.8%
8.7 1
 
0.8%
13 1
 
0.8%
(Missing) 1
 
0.8%
ValueCountFrequency (%)
0.005 92
71.3%
0.0052 2
 
1.6%
0.0057 2
 
1.6%
1 28
 
21.7%
7.8 1
 
0.8%
8.7 1
 
0.8%
13 1
 
0.8%
44 1
 
0.8%
ValueCountFrequency (%)
44 1
 
0.8%
13 1
 
0.8%
8.7 1
 
0.8%
7.8 1
 
0.8%
1 28
 
21.7%
0.0057 2
 
1.6%
0.0052 2
 
1.6%
0.005 92
71.3%

cd_total_mg_l_menor_que
Categorical

High correlation 

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0.001
98 
0.005
27 
0.01
 
4

Length

Max length5
Median length5
Mean length4.9689922
Min length4

Characters and Unicode

Total characters641
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.001
2nd row0.005
3rd row0.001
4th row0.001
5th row0.005

Common Values

ValueCountFrequency (%)
0.001 98
76.0%
0.005 27
 
20.9%
0.01 4
 
3.1%

Length

2024-11-03T17:51:22.454233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T17:51:22.709768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.001 98
76.0%
0.005 27
 
20.9%
0.01 4
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 383
59.8%
. 129
 
20.1%
1 102
 
15.9%
5 27
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 512
79.9%
Other Punctuation 129
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 383
74.8%
1 102
 
19.9%
5 27
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 641
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 383
59.8%
. 129
 
20.1%
1 102
 
15.9%
5 27
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 383
59.8%
. 129
 
20.1%
1 102
 
15.9%
5 27
 
4.2%

clorofila_a_ug_l
Real number (ℝ)

High correlation  Zeros 

Distinct39
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024329922
Minimum0
Maximum0.84016
Zeros11
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:23.020179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.01
Q30.01
95-th percentile0.079152
Maximum0.84016
Range0.84016
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.079239101
Coefficient of variation (CV)3.2568579
Kurtosis89.552139
Mean0.024329922
Median Absolute Deviation (MAD)0
Skewness8.9234281
Sum3.13856
Variance0.0062788352
MonotonicityNot monotonic
2024-11-03T17:51:23.356844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.01 70
54.3%
0 11
 
8.5%
0.011 3
 
2.3%
0.001 3
 
2.3%
0.00415 2
 
1.6%
0.00119 2
 
1.6%
0.013 2
 
1.6%
0.018 2
 
1.6%
0.005 2
 
1.6%
0.00237 2
 
1.6%
Other values (29) 30
23.3%
ValueCountFrequency (%)
0 11
8.5%
0.001 3
 
2.3%
0.00119 2
 
1.6%
0.00178 1
 
0.8%
0.00237 2
 
1.6%
0.00356 1
 
0.8%
0.00415 2
 
1.6%
0.005 2
 
1.6%
0.006 1
 
0.8%
0.007 1
 
0.8%
ValueCountFrequency (%)
0.84016 1
0.8%
0.221 1
0.8%
0.21265 1
0.8%
0.131 1
0.8%
0.11036 1
0.8%
0.09909 1
0.8%
0.08366 1
0.8%
0.07239 1
0.8%
0.06289 1
0.8%
0.061 1
0.8%

microcistina_ug_l
Categorical

High correlation 

Distinct4
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0.2
71 
1.0
29 
0.15
28 
0.8
 
1

Length

Max length4
Median length3
Mean length3.2170543
Min length3

Characters and Unicode

Total characters415
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row0.15
2nd row1.0
3rd row0.2
4th row0.2
5th row1.0

Common Values

ValueCountFrequency (%)
0.2 71
55.0%
1.0 29
22.5%
0.15 28
 
21.7%
0.8 1
 
0.8%

Length

2024-11-03T17:51:23.674856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T17:51:23.900951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.2 71
55.0%
1.0 29
22.5%
0.15 28
 
21.7%
0.8 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 129
31.1%
. 129
31.1%
2 71
17.1%
1 57
13.7%
5 28
 
6.7%
8 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 286
68.9%
Other Punctuation 129
31.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 129
45.1%
2 71
24.8%
1 57
19.9%
5 28
 
9.8%
8 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 415
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 129
31.1%
. 129
31.1%
2 71
17.1%
1 57
13.7%
5 28
 
6.7%
8 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 415
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 129
31.1%
. 129
31.1%
2 71
17.1%
1 57
13.7%
5 28
 
6.7%
8 1
 
0.2%

ica
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.891473
Minimum26
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-11-03T17:51:24.132253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile29.4
Q138
median44
Q350
95-th percentile63.2
Maximum74
Range48
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.8196553
Coefficient of variation (CV)0.21874211
Kurtosis0.18021438
Mean44.891473
Median Absolute Deviation (MAD)6
Skewness0.49095474
Sum5791
Variance96.42563
MonotonicityNot monotonic
2024-11-03T17:51:24.347552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
49 9
 
7.0%
42 8
 
6.2%
46 8
 
6.2%
43 7
 
5.4%
38 6
 
4.7%
41 6
 
4.7%
39 6
 
4.7%
48 5
 
3.9%
44 5
 
3.9%
34 5
 
3.9%
Other values (30) 64
49.6%
ValueCountFrequency (%)
26 3
2.3%
28 3
2.3%
29 1
 
0.8%
30 2
 
1.6%
31 1
 
0.8%
33 2
 
1.6%
34 5
3.9%
35 4
3.1%
36 3
2.3%
37 3
2.3%
ValueCountFrequency (%)
74 1
 
0.8%
72 1
 
0.8%
67 1
 
0.8%
65 2
1.6%
64 2
1.6%
62 1
 
0.8%
61 1
 
0.8%
60 3
2.3%
59 1
 
0.8%
58 4
3.1%

calidad_de_agua
Categorical

High correlation 

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Extremadamente deteriorada
69 
Muy deteriorada
55 
Deteriorada
 
5

Length

Max length26
Median length26
Mean length20.728682
Min length11

Characters and Unicode

Total characters2674
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExtremadamente deteriorada
2nd rowMuy deteriorada
3rd rowMuy deteriorada
4th rowMuy deteriorada
5th rowExtremadamente deteriorada

Common Values

ValueCountFrequency (%)
Extremadamente deteriorada 69
53.5%
Muy deteriorada 55
42.6%
Deteriorada 5
 
3.9%

Length

2024-11-03T17:51:24.619280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T17:51:24.805260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
deteriorada 129
51.0%
extremadamente 69
27.3%
muy 55
21.7%

Most occurring characters

ValueCountFrequency (%)
e 465
17.4%
a 396
14.8%
r 327
12.2%
d 322
12.0%
t 267
10.0%
m 138
 
5.2%
i 129
 
4.8%
o 129
 
4.8%
124
 
4.6%
E 69
 
2.6%
Other values (6) 308
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2421
90.5%
Uppercase Letter 129
 
4.8%
Space Separator 124
 
4.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 465
19.2%
a 396
16.4%
r 327
13.5%
d 322
13.3%
t 267
11.0%
m 138
 
5.7%
i 129
 
5.3%
o 129
 
5.3%
x 69
 
2.9%
n 69
 
2.9%
Other values (2) 110
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
E 69
53.5%
M 55
42.6%
D 5
 
3.9%
Space Separator
ValueCountFrequency (%)
124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2550
95.4%
Common 124
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 465
18.2%
a 396
15.5%
r 327
12.8%
d 322
12.6%
t 267
10.5%
m 138
 
5.4%
i 129
 
5.1%
o 129
 
5.1%
E 69
 
2.7%
x 69
 
2.7%
Other values (5) 239
9.4%
Common
ValueCountFrequency (%)
124
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2674
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 465
17.4%
a 396
14.8%
r 327
12.2%
d 322
12.0%
t 267
10.0%
m 138
 
5.2%
i 129
 
4.8%
o 129
 
4.8%
124
 
4.6%
E 69
 
2.6%
Other values (6) 308
11.5%

Interactions

2024-11-03T17:51:04.785259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:04.341780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:06.831786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:12.064652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:16.541373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:21.210213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:24.868786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:30.181218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:34.725959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:38.901937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:42.590336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:45.340026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:49.152059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:52.318559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:54.809601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:56.783425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:59.510928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:01.781571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:04.917060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:04.455906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:07.062899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:12.351683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:16.722617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:21.381279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:25.068893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:30.374645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:35.464277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:39.096178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:42.751391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:45.619929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:49.278425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:52.450190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:54.910797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:56.875608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:59.608744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:01.956135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:05.033025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:04.574632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:07.292773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:12.524234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:16.942036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:21.584207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:25.275228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:30.583141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:35.648015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:39.260893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:42.941955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:45.867893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:49.422237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:52.644712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:55.013059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:56.975627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:59.717015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:02.140230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:05.150415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:04.716932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:07.599095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:12.701769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:17.236054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:21.783102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:25.476918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:30.797649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:35.960030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:39.561601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:43.144947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:46.136106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:49.593125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:52.793925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:55.114774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:57.091866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:59.820251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:02.315298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:05.250685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:04.894615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:07.865244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:12.890571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:17.528168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:22.351009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:25.766613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:30.993802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:36.272654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:39.781526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:43.296891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:46.371756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:49.769341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:52.952115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:55.231775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:57.190892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:59.931984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:02.466776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:05.380508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:05.032019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:08.135003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:13.113712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:17.803662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:22.575395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-03T17:50:34.179365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:38.503709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:42.292654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:45.050002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:48.882188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:52.066741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:54.598661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:56.580919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:59.245321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:01.480688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:04.551268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:07.282168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:06.600690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:11.773700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:16.349619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:21.032955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:24.669941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:29.930659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:34.451085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:38.694630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:42.448903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:45.209327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:49.027550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:52.197383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:54.709950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:56.693822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:50:59.389699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:01.618186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T17:51:04.648444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-03T17:51:24.965583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
calidad_de_aguacampañacd_total_mg_l_menor_queclorofila_a_ug_lcolif_fecales_ufc_100mlcolorcr_total_mg_ldbo_mg_ldqo_mg_lenteroc_ufc_100mlescher_coli_ufc_100mlespumasfosf_ortofos_mg_lhidr_deriv_petr_ug_licamat_suspmicrocistina_ug_lnh4_mg_lnitrato_mg_lodoloresp_total_l_mg_lphsitiostem_aguatem_aireturbiedad_ntu
calidad_de_agua1.0000.1670.1150.0490.2760.2930.0000.0000.0490.0730.0980.2050.1980.1050.9490.2150.1650.1820.1660.2700.2590.2220.1330.3090.0650.1590.000
campaña0.1671.0000.6700.0000.0000.0470.1160.3100.5980.0380.0000.0300.1890.4470.2440.3690.8120.0950.0000.2150.0000.3940.0930.0000.5820.6000.410
cd_total_mg_l_menor_que0.1150.6701.0000.0000.3430.0870.0000.2120.6370.2320.3390.0730.0000.6800.1310.1790.6680.2240.0000.0000.0000.1790.0600.0000.4290.3330.404
clorofila_a_ug_l0.0490.0000.0001.000-0.0040.0000.1670.5120.198-0.119-0.1080.0000.1190.0020.0590.0000.0000.0270.0970.2980.000-0.0200.3820.000-0.260-0.213-0.043
colif_fecales_ufc_100ml0.2760.0000.343-0.0041.0000.3910.1590.379-0.1110.5640.7750.5060.376-0.044-0.8240.0000.0000.354-0.207-0.3220.4040.217-0.2040.194-0.089-0.114-0.242
color0.2930.0470.0870.0000.3911.0000.0000.1600.0000.3180.4050.4150.4020.0000.7050.4700.0000.4570.2800.3360.6670.3900.1320.5920.3220.2250.000
cr_total_mg_l0.0000.1160.0000.1670.1590.0001.0000.262-0.5790.0970.1740.0000.142-0.237-0.1100.0000.1230.0410.2320.1540.000-0.0680.1780.000-0.216-0.2940.395
dbo_mg_l0.0000.3100.2120.5120.3790.1600.2621.000-0.0330.2350.3100.2770.3420.066-0.3230.1500.1200.427-0.0930.0920.2140.1310.2550.000-0.426-0.420-0.168
dqo_mg_l0.0490.5980.6370.198-0.1110.000-0.579-0.0331.000-0.083-0.2320.192-0.0930.3240.0530.1300.549-0.043-0.2100.0600.000-0.0330.0630.0000.1790.418-0.336
enteroc_ufc_100ml0.0730.0380.232-0.1190.5640.3180.0970.235-0.0831.0000.6690.4780.3410.134-0.7190.1880.0000.374-0.039-0.3240.3030.288-0.2040.2310.0480.008-0.169
escher_coli_ufc_100ml0.0980.0000.339-0.1080.7750.4050.1740.310-0.2320.6691.0000.3390.435-0.050-0.8080.0000.0000.465-0.131-0.4790.3830.297-0.3270.1070.0910.017-0.145
espumas0.2050.0300.0730.0000.5060.4150.0000.2770.1920.4780.3391.0000.4510.0000.5350.1890.0140.5120.0000.1120.4930.4080.0000.2230.0000.3820.145
fosf_ortofos_mg_l0.1980.1890.0000.1190.3760.4020.1420.342-0.0930.3410.4350.4511.000-0.218-0.5290.3340.1870.526-0.225-0.4660.4090.778-0.1570.284-0.004-0.078-0.179
hidr_deriv_petr_ug_l0.1050.4470.6800.002-0.0440.000-0.2370.0660.3240.134-0.0500.000-0.2181.000-0.0080.0000.447-0.095-0.0700.0860.000-0.136-0.1520.0000.1070.131-0.131
ica0.9490.2440.1310.059-0.8240.705-0.110-0.3230.053-0.719-0.8080.535-0.529-0.0081.0000.3260.150-0.5430.1960.5290.708-0.4880.2950.212-0.105-0.0600.115
mat_susp0.2150.3690.1790.0000.0000.4700.0000.1500.1300.1880.0000.1890.3340.0000.3261.0000.2990.2690.0000.2730.2230.4480.0440.2450.4130.3660.000
microcistina_ug_l0.1650.8120.6680.0000.0000.0000.1230.1200.5490.0000.0000.0140.1870.4470.1500.2991.0000.1000.0000.0000.0000.3820.0550.0000.3990.4240.410
nh4_mg_l0.1820.0950.2240.0270.3540.4570.0410.427-0.0430.3740.4650.5120.526-0.095-0.5430.2690.1001.000-0.353-0.5190.4770.375-0.2210.222-0.111-0.163-0.298
nitrato_mg_l0.1660.0000.0000.097-0.2070.2800.232-0.093-0.210-0.039-0.1310.000-0.225-0.0700.1960.0000.000-0.3531.0000.3690.294-0.0330.4260.4730.1660.0140.255
od0.2700.2150.0000.298-0.3220.3360.1540.0920.060-0.324-0.4790.112-0.4660.0860.5290.2730.000-0.5190.3691.0000.442-0.4520.6750.241-0.384-0.2670.168
olores0.2590.0000.0000.0000.4040.6670.0000.2140.0000.3030.3830.4930.4090.0000.7080.2230.0000.4770.2940.4421.0000.3620.2410.7060.0940.1420.000
p_total_l_mg_l0.2220.3940.179-0.0200.2170.390-0.0680.131-0.0330.2880.2970.4080.778-0.136-0.4880.4480.3820.375-0.033-0.4520.3621.000-0.0950.1440.1980.067-0.014
ph0.1330.0930.0600.382-0.2040.1320.1780.2550.063-0.204-0.3270.000-0.157-0.1520.2950.0440.055-0.2210.4260.6750.241-0.0951.0000.246-0.368-0.2270.109
sitios0.3090.0000.0000.0000.1940.5920.0000.0000.0000.2310.1070.2230.2840.0000.2120.2450.0000.2220.4730.2410.7060.1440.2461.0000.1940.0000.152
tem_agua0.0650.5820.429-0.260-0.0890.322-0.216-0.4260.1790.0480.0910.000-0.0040.107-0.1050.4130.399-0.1110.166-0.3840.0940.198-0.3680.1941.0000.8000.065
tem_aire0.1590.6000.333-0.213-0.1140.225-0.294-0.4200.4180.0080.0170.382-0.0780.131-0.0600.3660.424-0.1630.014-0.2670.1420.067-0.2270.0000.8001.000-0.017
turbiedad_ntu0.0000.4100.404-0.043-0.2420.0000.395-0.168-0.336-0.169-0.1450.145-0.179-0.1310.1150.0000.410-0.2980.2550.1680.000-0.0140.1090.1520.065-0.0171.000

Missing values

2024-11-03T17:51:07.678610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-03T17:51:08.881618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-03T17:51:09.791481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sitioscampañatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntuhidr_deriv_petr_ug_lcr_total_mg_lcd_total_mg_l_menor_queclorofila_a_ug_lmicrocistina_ug_licacalidad_de_agua
0Canal Villanueva y Río Lujánverano25.627.03.916.96FalseFalseFalseTrue400010003306.50.400.920.372.030.060.0100.00.0050.0010.000000.1542Extremadamente deteriorada
1Canal Villanueva y Río Lujánotoño15.613.08.286.79FalseFalseFalseFalse4000200503.00.980.200.204.1NaN30.0110.01.0000.0050.010001.0048Muy deteriorada
2Canal Villanueva y Río Lujáninvierno14.813.09.907.09FalseFalseFalseFalse1000200682.11.100.200.102.030.027.0100.00.0050.0010.010000.2064Muy deteriorada
3Canal Villanueva y Río Lujánprimavera24.429.07.286.91FalseFalseFalseTrue400200652.70.440.270.122.031.045.0100.00.0050.0010.003560.2055Muy deteriorada
5Río Lujan y Arroyo Caraguatáotoño15.713.06.907.00FalseFalseFalseFalse3000010008002.90.570.190.204.3NaN30.0150.01.0000.0050.010001.0039Extremadamente deteriorada
6Río Lujan y Arroyo Caraguatáinvierno14.513.02.546.80FalseFalseFalseFalse1000020001502.04.700.480.576.830.022.0100.00.0050.0010.013000.2043Extremadamente deteriorada
7Río Lujan y Arroyo Caraguatáprimavera25.433.06.206.90FalseFalseFalseTrue20001000584.23.300.200.142.037.036.0100.00.0050.0010.004150.2048Muy deteriorada
8Canal Aliviador y Río Lujanverano24.624.01.217.12TrueTrueTrueTrue200001800046002.86.000.680.566.330.017.0100.00.0050.0010.013000.1526Extremadamente deteriorada
9Canal Aliviador y Río Lujanotoño15.713.04.827.04FalseFalseFalseFalse800006000056002.94.300.510.514.85.630.070.01.0000.0050.010001.0036Extremadamente deteriorada
10Canal Aliviador y Río Lujaninvierno14.714.02.306.80TrueFalseFalseFalse30000100002402.03.000.570.585.030.021.0100.00.0050.0010.010000.2034Extremadamente deteriorada
sitioscampañatem_aguatem_aireodpholorescolorespumasmat_suspcolif_fecales_ufc_100mlescher_coli_ufc_100mlenteroc_ufc_100mlnitrato_mg_lnh4_mg_lp_total_l_mg_lfosf_ortofos_mg_ldbo_mg_ldqo_mg_lturbiedad_ntuhidr_deriv_petr_ug_lcr_total_mg_lcd_total_mg_l_menor_queclorofila_a_ug_lmicrocistina_ug_licacalidad_de_agua
153Playa La Bagliardiverano20.026.05.097.43FalseFalseFalseTrue10003603003.55.401.401.103.973.018.0100.00.0050.0010.005000.1542Extremadamente deteriorada
155Playa La Bagliardiinvierno10.012.06.848.14TrueTrueTrueTrue1000003000062002.013.001.501.505.5125.08.7100.00.0050.0010.010000.2028Extremadamente deteriorada
156Balneario Municipalverano21.027.05.897.79FalseFalseFalseFalse400201803.30.980.781.102.430.0210.0100.013.0000.0050.011000.1549Muy deteriorada
158Balneario Municipalinvierno8.010.09.708.04FalseFalseFalseFalse15000150010004.20.200.560.447.030.029.0100.00.0050.0010.010000.2041Extremadamente deteriorada
159Playa La Bagliardiprimavera11.019.05.707.78FalseTrueFalseTrue100007003002.00.901.101.203.050.04.9100.00.0050.0010.010000.2039Extremadamente deteriorada
160Balneario Municipalprimavera11.019.07.588.21FalseFalseFalseFalse110290154.10.410.310.272.557.070.0100.00.0050.0010.041530.2062Muy deteriorada
161Playa La Balandraverano20.026.04.897.75FalseFalseFalseFalse12004002403.31.800.730.422.730.05.3100.00.0050.0010.006000.1546Muy deteriorada
163Playa La Balandrainvierno7.09.09.999.22FalseFalseFalseFalse30020202.01.300.330.235.030.075.0100.00.0050.0010.010000.2046Muy deteriorada
164Playa La Balandrainvierno7.08.010.709.19FalseFalseFalseFalse4200802402.00.700.450.227.337.075.0100.00.0050.0010.010000.2043Extremadamente deteriorada
165Playa La Balandraprimavera11.019.08.298.39FalseTrueFalseTrue360017023.90.110.200.113.058.050.0100.00.0050.0010.083660.2049Muy deteriorada